Three-Dimensional Imaging Method for Concealed Human Targets Based on Array Stitching
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摘要: 在进行墙后人体目标三维成像时，系统需具备获取距离、方位和俯仰三维信息的能力。然而，通常采用的二维平面阵列存在通道数量多导致的校准难、成本高等问题。为此，该文提出了一种基于阵列拼接的遮蔽人体目标三维成像方法，通过单台小孔径雷达分时顺序拼接或多台独立工作的雷达同时空间拼接，实现低成本化的穿墙遮蔽人体目标三维成像。具体而言，首先通过三维加权总变分方法将各个横向阵列及纵向阵列的雷达回波数据进行初步成像；然后，将各个横向阵列获取的成像结果与纵向阵列的成像结果乘性融合，并采用Lucy-Richardson反卷积算法进一步提升成像分辨率；最后采用三维小波变换融合方法，实现各个子图像的有效融合。仿真与实测实验验证了所提方法的有效性，为墙后人体目标三维成像提供了一种新颖、低成本的解决方案。
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关键词:
- 遮蔽人体目标三维成像 /
- 阵列拼接 /
- 三维加权总变分 /
- Lucy-Richardson反卷积 /
- 三维小波变换
Abstract:Objective Traditional Through-the-Wall Radar (TWR) systems based on planar multiple-input multiple-output arrays often face high hardware complexity, calibration challenges, and increased system cost. To overcome these limitations, we propose a Three-Dimensional (3D) imaging framework based on array stitching. The method uses either time-sequential or simultaneous operation of multiple small-aperture radar sub-arrays to emulate a large aperture. This strategy substantially reduces hardware complexity while maintaining accurate 3D imaging of concealed human targets. Methods The proposed framework integrates three core techniques: 3D weighted total variation (3DWTV) reconstruction, Lucy–Richardson (LR) deconvolution, and 3D wavelet transform (3DWT)-based fusion. Radar echoes are first collected from horizontally and vertically distributed sub-arrays that emulate a planar aperture. Each sub-array image is independently reconstructed using 3DWTV, which enforces spatial sparsity to suppress noise while preserving structural details. The horizontal and vertical images are then multiplicatively fused to jointly recover azimuth and elevation information. To reduce diffraction-induced blurring, LR deconvolution models system degradation through the Point Spread Function (PSF) and iteratively refines scene reflectivity, thereby enhancing cross-range resolution. Finally, 3DWT decomposes the images into multi-scale sub-bands (e.g., LLL, LLH, LHL), which are selectively fused using absolute-maximum and fuzzy-logic rules. The inverse wavelet transform is then applied to reconstruct the final 3D image, retaining both global and local features. Results and Discussions The proposed method is evaluated through both simulations and real-world experiments using a Stepped-Frequency Continuous-Wave (SFCW) radar operating from 1.6 to 2.2 GHz with a 2Tx–4Rx configuration. In simulations, compared with baseline algorithms such as Back-Projection (BP) and the Fast Iterative Shrinkage-Thresholding Algorithm (FISTA), the proposed method achieves better performance. Image Entropy (IE) decreases from 9.7125 for BP and9.7065 for FISTA to8.0711 , which reflects improved image quality. Experimental tests conducted in indoor environments further confirm robustness. For both standing and sitting postures, IE is reduced from9.9982 to7.0030 and from9.9947 to6.2261 , respectively.Conclusions This study presents a low-cost, high-resolution 3D imaging method for TWR systems based on array stitching. By integrating 3DWTV reconstruction, LR deconvolution, and 3DWT fusion, the method effectively reconstructs concealed human postures using a limited aperture. The approach simplifies hardware design, reduces system complexity, and preserves imaging quality under sparse sampling, thereby providing a practical solution for portable and scalable TWR systems. -
1 三维加权总变分成像算法
输入：采样后回波$ {{\boldsymbol{z}}^r} $，字典矩阵$ {{\boldsymbol{\varTheta }}^r} $，正则化参数$ \mu $，惩罚因子
$ \beta $(1)初始化：$ {\left( {{{\boldsymbol{s}}^r}} \right)^{\left( 0 \right)}} $, $ {{\boldsymbol{O}}^{\left( 0 \right)}} $, $ {{\boldsymbol{\lambda }}^{\left( 0 \right)}} $ (2)当不满足${\left\| {{{\left( {{{\boldsymbol{s}}^r}} \right)}^{\left( {j + 1} \right)}} - {{\left( {{{\boldsymbol{s}}^r}} \right)}^{\left( j \right)}}} \right\|_2} < \delta $或最大迭代次数时，
执行：通过梯度下降法更新$ {\left( {{{\boldsymbol{s}}^r}} \right)^{\left( {j + 1} \right)}} $； 通过式(11)更新$ {\boldsymbol{O}}_{}^{\left( {j + 1} \right)} $； 通过式(13)更新$ {\boldsymbol{\lambda }}_{}^{\left( {k + 1} \right)} $； 输出：重建图像${{\boldsymbol{s}}^r}$ 
下载: 导出CSV
表 1 图像指标对比
成像方法 IE指标值 纵向阵列1乘性融合结果 9.3126 纵向阵列2乘性融合结果 8.9860 纵向阵列1乘性融合后反卷积结果 7.8171 纵向阵列2乘性融合后反卷积结果 7.2108 BP算法结合像素级融合成像结果 9.7125 FISTA算法结合像素级融合成像结果 9.7065 所提算法结果 8.0711
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表 2 不同姿态下图像指标对比
姿态 IE BP算法结合
像素级融合FISTA算法结合
像素级融合所提
方法站姿 9.9982 8.4990 7.0030 坐姿 9.9947 7.8411 6.2261
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